Multiscale prediction of functional self-assembled materials using machine learning: High-performance surfactant molecules

Takuya Inokuchi, Na Li, Kei Morohoshi, Noriyoshi Arai

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Various physical properties of functional materials can be induced by controlling their chemical molecular structures. Therefore, molecular design is crucial in the fields of engineering and materials science. With its remarkable development in various fields, machine learning combined with molecular simulation has recently been found to be effective at predicting the electronic structure of materials (Nat. Commun., 2017, 8, 872 and Nat. Commun., 2017, 8, 13890). However, previous studies have used similar microscale information as input and output data for machine learning, i.e., molecular structures and electronic structures. In this study, we determined whether multiscale data can be predicted using machine learning via a self-assembly functional material system. In particular, we investigated whether machine learning can be used to predict dispersion and viscosity, as the representative physical properties of a self-assembled surfactant solution, from the chemical molecular structures of a surfactant. The results showed that relatively accurate information on these physical properties can be predicted from the molecular structure, suggesting that machine learning can be used to predict multiscale systems, such as surfactant molecules, self-assembled micelle structures, and physical properties of solutions. The results of this study will aid in further development of the application of machine learning to materials science and molecular design.

Original languageEnglish
Pages (from-to)16013-16021
Number of pages9
JournalNanoscale
Volume10
Issue number34
DOIs
Publication statusPublished - 2018 Sep 14
Externally publishedYes

Fingerprint

Surface-Active Agents
Learning systems
Surface active agents
Molecular structure
Molecules
Physical properties
Functional materials
Materials science
Electronic structure
Molecular electronics
Micelles
Self assembly
Viscosity

ASJC Scopus subject areas

  • Materials Science(all)

Cite this

Multiscale prediction of functional self-assembled materials using machine learning : High-performance surfactant molecules. / Inokuchi, Takuya; Li, Na; Morohoshi, Kei; Arai, Noriyoshi.

In: Nanoscale, Vol. 10, No. 34, 14.09.2018, p. 16013-16021.

Research output: Contribution to journalArticle

@article{6d6c0d0b51fb444691795ca6cda47b77,
title = "Multiscale prediction of functional self-assembled materials using machine learning: High-performance surfactant molecules",
abstract = "Various physical properties of functional materials can be induced by controlling their chemical molecular structures. Therefore, molecular design is crucial in the fields of engineering and materials science. With its remarkable development in various fields, machine learning combined with molecular simulation has recently been found to be effective at predicting the electronic structure of materials (Nat. Commun., 2017, 8, 872 and Nat. Commun., 2017, 8, 13890). However, previous studies have used similar microscale information as input and output data for machine learning, i.e., molecular structures and electronic structures. In this study, we determined whether multiscale data can be predicted using machine learning via a self-assembly functional material system. In particular, we investigated whether machine learning can be used to predict dispersion and viscosity, as the representative physical properties of a self-assembled surfactant solution, from the chemical molecular structures of a surfactant. The results showed that relatively accurate information on these physical properties can be predicted from the molecular structure, suggesting that machine learning can be used to predict multiscale systems, such as surfactant molecules, self-assembled micelle structures, and physical properties of solutions. The results of this study will aid in further development of the application of machine learning to materials science and molecular design.",
author = "Takuya Inokuchi and Na Li and Kei Morohoshi and Noriyoshi Arai",
year = "2018",
month = "9",
day = "14",
doi = "10.1039/c8nr03332c",
language = "English",
volume = "10",
pages = "16013--16021",
journal = "Nanoscale",
issn = "2040-3364",
publisher = "Royal Society of Chemistry",
number = "34",

}

TY - JOUR

T1 - Multiscale prediction of functional self-assembled materials using machine learning

T2 - High-performance surfactant molecules

AU - Inokuchi, Takuya

AU - Li, Na

AU - Morohoshi, Kei

AU - Arai, Noriyoshi

PY - 2018/9/14

Y1 - 2018/9/14

N2 - Various physical properties of functional materials can be induced by controlling their chemical molecular structures. Therefore, molecular design is crucial in the fields of engineering and materials science. With its remarkable development in various fields, machine learning combined with molecular simulation has recently been found to be effective at predicting the electronic structure of materials (Nat. Commun., 2017, 8, 872 and Nat. Commun., 2017, 8, 13890). However, previous studies have used similar microscale information as input and output data for machine learning, i.e., molecular structures and electronic structures. In this study, we determined whether multiscale data can be predicted using machine learning via a self-assembly functional material system. In particular, we investigated whether machine learning can be used to predict dispersion and viscosity, as the representative physical properties of a self-assembled surfactant solution, from the chemical molecular structures of a surfactant. The results showed that relatively accurate information on these physical properties can be predicted from the molecular structure, suggesting that machine learning can be used to predict multiscale systems, such as surfactant molecules, self-assembled micelle structures, and physical properties of solutions. The results of this study will aid in further development of the application of machine learning to materials science and molecular design.

AB - Various physical properties of functional materials can be induced by controlling their chemical molecular structures. Therefore, molecular design is crucial in the fields of engineering and materials science. With its remarkable development in various fields, machine learning combined with molecular simulation has recently been found to be effective at predicting the electronic structure of materials (Nat. Commun., 2017, 8, 872 and Nat. Commun., 2017, 8, 13890). However, previous studies have used similar microscale information as input and output data for machine learning, i.e., molecular structures and electronic structures. In this study, we determined whether multiscale data can be predicted using machine learning via a self-assembly functional material system. In particular, we investigated whether machine learning can be used to predict dispersion and viscosity, as the representative physical properties of a self-assembled surfactant solution, from the chemical molecular structures of a surfactant. The results showed that relatively accurate information on these physical properties can be predicted from the molecular structure, suggesting that machine learning can be used to predict multiscale systems, such as surfactant molecules, self-assembled micelle structures, and physical properties of solutions. The results of this study will aid in further development of the application of machine learning to materials science and molecular design.

UR - http://www.scopus.com/inward/record.url?scp=85052835888&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052835888&partnerID=8YFLogxK

U2 - 10.1039/c8nr03332c

DO - 10.1039/c8nr03332c

M3 - Article

AN - SCOPUS:85052835888

VL - 10

SP - 16013

EP - 16021

JO - Nanoscale

JF - Nanoscale

SN - 2040-3364

IS - 34

ER -